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2025 IEEE International Conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Fan, Shitong Xiao, Feiyang Wang, Wenbo Qi, Shuhan Zhu, Qiaoxi Wang, Wenwu Guan, Jian Group of Intelligent Signal Processing Harbin Engineering University Harbin China Faculty of Computing Harbin Institute of Technology Harbin China School of Computer Science and Technology Harbin Institute of Technology Shenzhen China Acoustics Lab University of Technology Sydney Ultimo Australia Centre for Vision Speech and Signal Processing University of Surrey Guildford United Kingdom

ISBN: (纸本)9798350368741

Microphone array techniques are widely used in sound source localization and smart city acoustic-based traffic monitoring, but these applications face significant challenges due to the scarcity of lab.led real-world traffic audio data and the complexity and diversity of application scenarios. The DCASE Challenge's Task 10 focuses on using multi-channel audio signals to count vehicles (cars or commercial vehicles) and identify their directions (left-to-right or vice versa). In this paper, we propose a graph-enhanced dual-stream feature fusion network (GEDF-Net) for acoustic traffic monitoring, which simultaneously considers vehicle type and direction to improve detection. We propose a graph-enhanced dual-stream feature fusion strategy which consists of a vehicle type feature extraction (VTFE) branch, a vehicle direction feature extraction (VDFE) branch, and a frame-level feature fusion module to combine the type and direction feature for enhanced performance. A pre-trained model (PANNs) is used in the VTFE branch to mitigate data scarcity and enhance the type features, followed by a graph attention mechanism to exploit temporal relationships and highlight important audio events within these features. The frame-level fusion of direction and type features enables fine-grained feature representation, resulting in better detection performance. Experiments demonstrate the effectiveness of our proposed method. GEDF-Net is our submission that achieved 1st place in the DCASE 2024 Challenge Task 10. © 2025 IEEE.

关键词： Acoustic-based traffic monitoring feature fusion graph attention pre-trained model transfer learning

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Disentangling Hierarchical Features for Anomalous Sound Detection Under Domain Shift

Disentangling Hierarchical Features for Anomalous Sound Dete...

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2025 IEEE International Conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Guan, Jian Tian, Jiantong Zhu, Qiaoxi Xiao, Feiyang Zhang, Hejing Liu, Xubo Group of Intelligent Signal Processing College of Computer Science and Technology Harbin Engineering University Harbin150001 China Acoustics Lab University of Technology Sydney UltimoNSW2007 Australia Centre for Vision Speech and Signal Processing University of Surrey GuildfordGU2 7XH United Kingdom

ISBN: (纸本)9798350368741

Anomalous sound detection (ASD) encounters difficulties with domain shift, where the sounds of machines in target domains differ significantly from those in source domains due to varying operating conditions. Existing methods typically employ domain classifiers to enhance detection performance, but they often overlook the influence of domain-unrelated information. This oversight can hinder the model's ability to clearly distinguish between domains, thereby weakening its capacity to differentiate normal from abnormal sounds. In this paper, we propose a Gradient Reversal-based Hierarchical feature Disentanglement (GRHD) method to address the above challenge. GRHD uses gradient reversal to separate domain-related features from domain-unrelated ones, resulting in more robust feature representations. Additionally, the method employs a hierarchical structure to guide the learning of fine-grained, domain-specific features by leveraging availab.e metadata, such as section IDs and machine sound attributes. Experimental results on the DCASE 2022 Challenge Task 2 dataset demonstrate that the proposed method significantly improves ASD performance under domain shift. © 2025 IEEE.

关键词： Anomalous sound detection domain shift feature learning gradient reversal

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Robust Adaptive Beamforming Based on MR-FDA-MIMO radar Jamming Suppression

Robust Adaptive Beamforming Based on MR-FDA-MIMO radar Jammi...

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2023 IEEE International Radar Conference, RADAR 2023

作者： Wu, Zhixia Zhu, Shengqi Xu, Jingwei Lan, Lan Xu, Yanhong Li, Ximin Gao, Jie Xidian University National Key Lab. of Radar Signal Processing Xi'an710071 China Xi'an University of Science and Technology School of Communication and Information Engineering Xi'an710071 China

ISBN: (纸本)9781665482783

In this work, an adaptive beamforming algorithm based on a minimum redundancy frequency diverse array multiple-input multiple-output (MR-FDA-MIMO) radar is proposed. The algorithm utilizes the transmit virtual domain signal of a MR-FDA-MIMO radar to perform transmit adaptive beamforming, overcoming the limitations of transmit array elements and suppressing multiple mainlobe deceptive jamming. To mitigate the beamforming performance degradation resulting from including the true target in the samples, the jamming plus noise covariance matrix is restructured to obtain the optimal performance of jamming suppression. To fully utilize the virtual degree of freedom (DOF) in the transmit dimension of MR-FDA-MIMO radar, sidelobe barrage jamming and mainlobe deceptive jamming are suppressed separately. Sidelobe barrage jamming is suppressed in the receive dimension. Multiple false targets are suppressed in the equivalent transmit dimension. The simulation results demonstrate the effectiveness of the algorithm. © 2023 IEEE.

关键词： Redundancy

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DATG: Data Augmentation with Transformer-Based Generation for Low-Resource Named Entity Recognition

DATG: Data Augmentation with Transformer-Based Generation fo...

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2023 China Automation Congress, CAC 2023

作者： Yili, Qian Haonan, Xu School of Computer and Information Technology Shanxi University Key Lab. of Computational Intelligence and Chinese Information Processing of Ministry of Education Taiyuan China School of Computer and Information Technology Shanxi University Taiyuan China

ISBN: (纸本)9798350303759

Data augmentation is an important technique for enhancing machine learning performance. In this study, we propose a novel generative data augmentation method for named entity recognition, which addresses the challenge of limited lab.led data in professional domains such as biomedical and chemical. The proposed method involves organizing input entities into a Transformer model to produce tagged sentences that are specifically related to the input entities. By leveraging external entity dictionaries, we effectively generate new tagged data and demonstrate its superiority over other data augmentation methods. Our approach offers a potential solution to the NER challenges faced in low-resource languages and specialized domains. © 2023 IEEE.

关键词： Data augmentation Named entity recognition Transformer

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Incomplete intuitionistic fuzzy behavioral group decision-making based on multigranulation probabilistic rough sets and MULTIMOORA for water quality inspection

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Journal of Intelligent and Fuzzy Systems 2023年第3期44卷 4537-4556页

作者： Bai, Wenhui Zhang, Chao Zhai, Yanhui Sangaiah, Arun Kumar School of Computer and Information Technology Key Lab. of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi University Shanxi Taiyuan China International Graduate Institute of Artificial Intelligence National Yunlin University of Science and Technology Taiwan Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon

Water quality inspection (WQI) is one of the primary ways to ensure the safe utilization of water resources, and complicated data modeling, fusion and analysis play a significant role in seeking the resource with the best water quality. Nevertheless, the challenges of missing data, relatively large differences in decision results and bounded rationality owned by decision-makers (DMs) in terms of WQI still exist nowadays. Thus, from the aspect of stable and behavioral decision-making in multi-granularity incomplete intuitionistic fuzzy information systems (MG-IIFISs), the paper investigates a comprehensive multi-attribute group decision-making (MAGDM) approach for the application of WQI. First, the concept of MG-IIFISs is built by modeling MAGDM problems with intuitionistic fuzzy numbers (IFNs), then a new transformation scheme is constructed for transforming MG-IIFISs into multi-granularity intuitionistic fuzzy information systems (MG-IFISs) based on the similarity principle. Second, three types of multigranulation intuitionistic fuzzy probabilistic rough sets (MG IF PRSs) are developed by referring to the MULTIMOORA (Multi-Objective Optimization by Ratio Analysis plus the full MULTIplicative form) method. Afterwards, attribute weights are objectively calculated based on the best-worst method (BWM), and a new stable and behavioral MAGDM approach is constructed by means of the TODIM (an acronym in Portuguese for interactive multi-criteria decision-making) method. At last, a case study in the setting of WQI is conducted with the support of a UCI data set, and sensitivity analysis, comparative analysis and experimental analysis are investigated to display the validity of the proposed approach. In general, the proposed approach improves the stability of decision results via MULTIMOORA and BWM, and also fully considers the bounded rationality of DMs' psychological behaviors from the aspect of the TODIM method, which has certain advantages in the community of MAGDM studie

关键词： Water quality

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Automatic Speech Recognition with BERT and CTC Transformers: A Review 2

Automatic Speech Recognition with BERT and CTC Transformers:...

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2nd International Conference on Electronics, Energy and Measurement, IC2EM 2023

作者： Djeffal, Noussaiba Kheddar, Hamza Addou, Djamel Mazari, Ahmed Cherif Himeur, Yassine Usthb University Speech and Signal Processing Lab. Algiers Algeria University of Medea Lsea Lab. Faculty of Technology Medea26000 Algeria University of Medea Lsea Lab Faculty of Science Medea26000 Algeria College of Engineering and Information Technology University of Dubai Dubai United Arab Emirates

ISBN: (纸本)9798350314243

This review paper provides a comprehensive analysis of recent advances in automatic speech recognition (ASR) with bidirectional encoder representations from transformers BERT and connectionist temporal classification (CTC) transformers. The paper first introduces the fundamental concepts of ASR and discusses the challenges associated with it. It then explains the architecture of BERT and CTC transformers and their potential applications in ASR. The paper reviews several studies that have used these models for speech recognition tasks and discusses the results obtained. Additionally, the paper highlights the limitations of these models and outlines potential areas for further research. All in all, this review provides valuable insights for researchers and practitioners who are interested in ASR with BERT and CTC transformers. © 2023 IEEE.

关键词： Speech recognition

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EEG based stress classification by using difference plots of variational modes and machine learning

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Journal of Ambient Intelligence and Humanized Computing 2023年第12期14卷 16347-16360页

作者： Salankar, Nilima Qaisar, Saeed Mian School of Computer Science University of Petroleum and Energy Studies Dehradun India Persistent System Limited IT Park Maharashtra Nagpur India Department of Electrical and Computer Engineering EFfat University Jeddah22332 Saudi Arabia Communication and Signal Processing Lab Energy and Technology Research Center Effat University Jeddah22332 Saudi Arabia

The recent trend in healthcare is to use the automated biomedical signals processing for an augmented and precise diagnosis. In this context, an original approach is presented for categorization of stress and non-stress classes by processing the multichannel Electroencephalogram (EEG) signals. The EEG signals are decomposed by using the "Empirical Mode Decomposition" (EMD) and "Variational Mode Decomposition" (VMD), respectively render the "Intrinsic Mode Functions" (IMFs) and Modes. The Second Order Difference Plots (SODPs) are traced for each IMF and Mode. Shapes of SODPs are used to discriminate the two intended categories. The feature space includes areas, Central Tendency Measures (CTMs) and means of SODPs. It is derived from first 7 IMFs and Modes. The identification of stress and non-stress categories is performed by using the robust machine learning algorithms namely, the Support Vector Machine (SVM), Boosting with Random Forest (RF) and Multilayer Perceptron Neural Network (MLPNN). The applicability is tested by using a publicly availab.e EEG signals dataset, recorded during mental arithmetic test. A detailed evaluation is carried out at the subset, individual channel and lobes wise. Noteworthy highest accuracies of subset wise 99.89%, channel wise 98.89% and lobe wise 99.99% are obtained respectively. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Empirical mode decomposition

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Research on Images Recognition based on Deep Convolution Neural Network 8

Research on Images Recognition based on Deep Convolution Neu...

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8th International Conference on Intelligent Computing and signal processing, ICSP 2023

作者： Chang, Xiaobo Zhao, Linlin Wang, Jinxiang Yanbian University College of Engineering Yanji China Yanbian University Intelligent Information Processing Lab. Dept. of Computer Science & Technology Yanji China College of Engineering Yanbian University Computer Science and Technology Yanji China

ISBN: (纸本)9798350302455

Image recognition has become a necessary component for computer visual system and widely utilized to detect objectives for downstream tasks in realistic applications. However, existing methods are concentrated on utilizing the clustering information of image features to recognize the subjects, which are unable to dispose several high correlation subjects and cost numerous computation period. In this paper, we utilize the convolution operation for images and extract the separated features. After acquiring these features, a deep neural network is established to recognize the objectives in the input images with enough iterations training procedures. Subsequently, the trained model is evaluated through the testing data-set to measure the real performance of proposed method. From our extensive experimental results, we can conclude that our proposed model can automatically realize the recognition process for input images with reasonable accuracy and acceptable computation costs. Additionally, our experimental results also indicate that the convolutional operation is more suitable to dispose the images data-set than traditional machine learning method. © 2023 IEEE.

关键词： Convolution

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Deep Learning Based Discovery of New Breast Cancer Stages: StyleGAN3 and Swin Transformer Approach

Deep Learning Based Discovery of New Breast Cancer Stages: S...

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Medical Technologies National Conference (TIPTEKNO)

作者： Reyhan Dede Gokhan Bilgin Department of Computer Engineering Signal and Image Processing Lab. (SIMPLAB) at YTU Yildiz Technical University (YTU) Istanbul Turkey

ISBN: (数字)9798331529819

ISBN: (纸本)9798331529826

This paper introduces a novel deep learning framework for the discovery of breast cancer stages, which integrates GAN-generated synthetic images with multi-omics data. By employing StyleGAN3 for the generation of realistic histopathological images and Swin Transformer for classification, the model draws upon both visual and biological data to enhance the accuracy of cancer staging predictions. The proposed methodology entails the generation of high-quality synthetic images using StyleGAN3, with a Fréchet Inception Distance (FID) score of 35, indicating a reasonable degree of similarity to real images. The images, in conjunction with RNA, miRNA, and clinical data, are integrated into a Swin Transformer-based classifier, resulting in an accuracy of 95.03 %, a precision of 95.00 %, and an F1 score of 95.00 %. A threshold-based softmax probability analysis was employed during the inference stage to explore the potential discovery of new cancer stages. The preliminary observation-based threshold of 30 % may be optimized through further experimentation. In the event that the model exhibited a confidence level for a given class below the specified threshold, the image was identified as a potential candidate for a previously unidentified stage. This study underscores the potential of multimodal data integration in enhancing breast cancer staging and offers insights into leveraging deep learning models for generating and classifying histopathological data, alongside identifying novel disease stages.

关键词： Deep learning Visualization Accuracy RNA Data integration Predictive models Transformers Breast cancer Data models Biomedical imaging

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Adversarial Recovery Network for Low-Light Person Re-Identification 23

Adversarial Recovery Network for Low-Light Person Re-Identif...

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6th International Conference on Image and Graphics processing, ICIGP 2023

作者： Xu, Liu Wang, Xiao Yuan, Xin Mao, Zhu Xu, Xin School of Computer Science and Technology Wuhan University of Science and Technology China School of Computer Science and Technology Wuhan University of Science and Technology ChinaHubei Prov. Key Lab. of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology China

ISBN: (纸本)9781450398572

Person re-identification (re-ID), which is to connect pedestrians with the same identity in different scenarios, has been more widely used in case detection of security. The re-ID datasets and technology have contributed greatly to advancing community research. There are currently no availab.e low-light datasets due to the difficulty of collecting and lab.ling images of people in low-light scenes. As a result, the vast majority of current re-ID methods do not have the recognition capability in low-light scenes. However, the recognition of persons in the low-light surveillance video has become an important issue to be solved for urban security, which plays an important role in improving the crime-solving rate of public safety departments and protecting people's lives and property. In this paper, we propose an Adversarial Recovery Network (ARN) that, unlike other approaches, enables a re-ID model with low-light recognition capability without the need for low-light data. Specifically, we first degrade the original images to generate multi-level low-light images;then obtain recovered images through the adversarial recovery module, and finally input the recovered images into the CNN network to extract pedestrian recognition knowledge. In addition, to validate the effectiveness of the proposed method, we also propose a low-light re-ID dataset (LOLR). Extensive experiments on LOLR have shown that our method outperforms other competitors by a wide margin. The dataset and code are availab.e online at https://***/xl02111/ARN. © 2023 ACM.

关键词： Recovery

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